Research and Projects

Research and Projects

Network Traffic Classification Using Deep Learning

Network traffic classification has become significantly important with rapid growth of current Internet network and online applications. There have been numerous studies on this topic which have led to many different approaches. Most of these approaches use predefined features extracted by an expert in order to classify network traffic. In contrast, in this project, we propose a deep learning based approach which integrates both feature extraction and classification phases into one system. Our proposed scheme, called “Deep Packet,” can handle both traffic characterization, in which the network traffic is categorized into major classes (e.g., FTP and P2P), and application identification in which identification of end-user applications (e.g., BitTorrent and Skype) is desired. Contrary to the most of current methods, Deep Packet can identify encrypted traffic and also distinguishes between VPN and non-VPN network traffic.

In this problem, we consider load balancing in a network of caching servers delivering contents to end users. Randomized load balancing via the so-called power of two choices is a well-known approach in parallel and distributed systems. In this framework, we investigate the tension between storage resources, communication cost, and load balancing performance. To this end, we propose a randomized load balancing scheme which simultaneously considers cache size limitation and proximity in the server redirection process.

Multi-terminal Secret Key Agreement

In this project, we study the problem of sharing a secret key among multiple parties from information theoretical point of view. More precisely, we consider a group of m trusted and authenticated nodes that aim to create a shared secret key K over a broadcast channel in the presence of an eavesdropper Eve. In addition, all of the trusted nodes can also discuss over a cost-free, noiseless and unlimited rate public channel which is also overheard by Eve. For this setup, we investigate the maximum achievable secret key generation rate and aim to propose efficient algorithms to achieve such rate.

Index Coding

In this work, we study the problem of linear index coding from graph homomorphism point of view. We show that the decision version of linear (scalar or vector) index coding problem is equivalent to certain graph homomorphism problem. Using this equivalence expression, we can recover some of the already known and prove some new results.